摘要
A new technique is proposed to solve the blind source separation (BSS) given only a single channel observation. The basis functions and the density of the coefficients of source signals learned by ICA are used as the prior knowledge. Based on the learned prior information the learning rules of single channel BSS are presented by maximizing the joint log likelihood of the mixed sources to obtain source signals from single observation, in which the posterior density of the given measurements is maximized. The experimental results exhibit a successful separation performance for mixtures of speech and music signals.
A new technique is proposed to solve the blind source separation （BSS） given only a single channel observation. The basis functions and the density of the coefficients of source signals learned by ICA are used as the prior knowledge. Based on the learned prior information the learning rules of single channel BSS are presented by maximizing the joint log likelihood of the mixed sources to obtain source signals from single observation, in which the posterior density of the given measurements is maximized. The experimental results exhibit a successful separation performance for mixtures of speech and music signals.
基金
Sponsored by the Research Foundation of Shanghai Municipal Education Commission(Grant No06FZ012 and 06FZ028)
关键词
盲源分离
独立成分分析
单渠道
极大可能性
blind source separation （BSS）
independent component analysis （ICA）
single channel
maximum likelihood
